During pregnancy, maternal nutrition and lifestyle play a critical role in influencing fetal development and newborn health outcomes. The aim of this study is to investigate the factors influencing the adherence to dietary patterns in pregnant women living in highly contaminated areas, and whether women with higher environmental risk perception manifest different nutritional behaviors during pregnancy. Food consumption data on 816 pregnant women from the Neonatal Environment and Health Outcomes (NEHO) residential birth cohort were analyzed.
View Article and Find Full Text PDFRisk perception (RP) evaluation during pregnancy and its relationship with lifestyles are considered useful tools for understanding communities living in high-risk areas and preventing dangerous exposure. It is well known that exposure to pollutants and less-healthy lifestyles may result in increased disease occurrence during life. Our work investigated environmental RP through ad hoc questionnaires administered to 611 mothers within the NEHO birth cohort, recruited in three heavily contaminated areas of Southern Italy.
View Article and Find Full Text PDFThe lifetime risk of developing symptomatic knee osteoarthritis is 60% in subjects with obesity. It is unclear which is the best weight loss interventions leading to a meaningful improvement of osteoarthritis symptoms and clinical conditions in subjects with obesity. Our network meta-analysis compares different weight loss interventions on the improvement of osteoarthritis symptoms and clinical conditions in subjects affected by obesity.
View Article and Find Full Text PDFPregnant women living in industrially contaminated sites (ICSs) are exposed to environmental contaminants through different pathways, and thus children's health may be affected by pollutants. We created the Neonatal Environment and Health Outcomes (NEHO) longitudinal birth cohort in three ICSs in the Mediterranean area of southern Italy, collecting comprehensive information on personal data and lifestyles by questionnaire. Through multiple correspondence analysis, we identified possible clusters of enrolled women, and a neural network classifier analysis (NNCA) was performed to identify variables capable of predicting the attrition rate of the study.
View Article and Find Full Text PDF